#!/usr/bin/python
# -*- coding:utf-8 -*-
# @FileName : Test2.py
# Author    : myh

import random
import torch
from d2l import torch as d2l


# 生成电流与电压数据
def synthetic_data(R, b, num_examples):
    """生成y=Xw+b+噪声"""
    I = torch.normal(0, 1, (num_examples, len(R)))
    U = torch.matmul(I, R) + b
    U += torch.normal(0, 0.01, U.shape)
    return I, U.reshape((-1, 1))


def data_iter(batch_size, features, labels):
    num_examples = len(features)
    indices = list(range(num_examples))
    # 这些样本是随机读取的，没有特定的顺序
    random.shuffle(indices)
    for i in range(0, num_examples, batch_size):
        batch_indices = torch.tensor(
            indices[i: min(i + batch_size, num_examples)])
        yield features[batch_indices], labels[batch_indices]


def integral_loss(U_hat, U):
    """积分损失"""
    return  (U_hat - U.reshape(U_hat.shape))**2/2


def linreg(I, R, b):  #@save
    """线性回归模型"""
    return torch.matmul(I, R) + b


def sgd(params, lr, batch_size):  #@save
    """小批量随机梯度下降"""
    with torch.no_grad():
        for param in params:
            param -= lr * param.grad / batch_size
            param.grad.zero_()


true_R = torch.tensor([2, 10.0])
true_b = 1
features, labels = synthetic_data(true_R, true_b, 1000)

print('features:', features[0],'\nlabel:', labels[0])

d2l.set_figsize()
d2l.plt.scatter(features[:, (1)].detach().numpy(), labels.detach().numpy(), 1)
d2l.plt.show()



batch_size = 19

# w = torch.normal(0, 0.01, size=(2,1), requires_grad=True)

R = torch.zeros(2,1, requires_grad=True)
b = torch.zeros(1, requires_grad=True)

integral_value = 0

lr = 0.01
num_epochs = 10
net = linreg
loss = integral_loss

for epoch in range(num_epochs):
    for I, U in data_iter(batch_size, features, labels):
        l = loss(net(I, R, b), U)  # X和y的小批量损失
        # 因为l形状是(batch_size,1)，而不是一个标量。l中的所有元素被加到一起，
        # 并以此计算关于[w,b]的梯度
        l.sum().backward()
        sgd([R, b], lr, batch_size)  # 使用参数的梯度更新参数
    with torch.no_grad():
        train_l = loss(net(features, R, b), labels)
        print(f'epoch {epoch + 1}, loss {float(train_l.mean()):f}')

print(f"b的差值为{true_b - b}")
print(f"R的差值为{true_R - R.detach().reshape(1,-1)}")
